AI / ML Infrastructure Engineer

AI / ML Infrastructure Engineer (MLOps) – Robotics - Hybrid in Bristol

We’re working with a cutting-edge robotics company building intelligent systems capable of learning real-world physical tasks.

They’re now hiring an AI / ML Infrastructure Engineer to own the end-to-end infrastructure that powers model training, data pipelines, and deployment into real-world robotic systems.

This is a highly technical role sitting at the intersection of machine learning, distributed systems, and robotics - not a generic MLOps position.

Key Responsibilities

Build and scale GPU-based training infrastructure for large ML workloads

Develop robust data pipelines for multi-modal datasets

Own experiment tracking, model versioning, and reproducibility

Design and optimise model deployment pipelines (including edge inference)

Improve CI/CD workflows for ML systems and automate infrastructure

Key Requirements

Strong Python and experience with PyTorch-based training pipelines

Experience with distributed training (DDP, FSDP, DeepSpeed)

Solid cloud experience (GCP / AWS / Azure)

Hands-on with Docker and infrastructure-as-code (Terraform)

Experience building ML pipelines in production environments

Desirable

Robotics, autonomous systems, or embodied AI experience

GPU orchestration (Kubeflow, Kubernetes, SkyPilot)

Edge deployment (ONNX, TensorRT)

Why Apply?

Work on real-world AI systems deployed into physical robots

Direct impact on cutting-edge robotics capability

Fast-moving, high-calibre engineering environment

Seniority Level

Not Applicable

Industry

IT Services and IT Consulting

Employment Type

Full-time

Job Functions

Information Technology Engineering Skills

Python (Programming Language)RoboticsArtificial Intelligence (AI)InfrastructureMachine Learning

Apply now!